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A reusable method for turning raw material (calls, meetings, notes, interviews, research) into narrative clusters, implied meanings, and multiple downstream outputs (briefs, letters, decks, databases).
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What this processor does
- Converts content → themes → implications → story arc → actionable artifacts
- Separates facts from meaning from decisions
- Produces consistent outputs you can reuse across projects
Inputs (what you provide)
- Source material (transcript, notes, doc, article, recording summary)
- Context (who/what/why now)
- Constraints (deadline, audience, format requirements)
- Desired outputs (choose from the library below)
Step-by-step method
1) Extract narrative clusters (theme segmentation)
Create 4–8 clusters that each answer:
- What is this part really about?
- What role does it play (gatekeeper, urgency, ritual, identity, etc.)?
Cluster template
- Label:
- Evidence (verbatim lines / bullets):
- What changes because of this (shift):
2) Derive implied meanings (subtext)